Yaoliang Yu  —  Publications by Year

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  2007 (1)
A Novel Facial Feature Point Localization Method on 3D Faces. Guan, P.; Yu, Y.; and Zhang, L. In IEEE Conference on Image Processing (ICIP), 2007.
A Novel Facial Feature Point Localization Method on 3D Faces [pdf] paper   link   bibtex  
  2009 (1)
A General Projection Property for Distribution Families. Yu, Y.; Li, Y.; Schuurmans, D.; and Szepesvári, C. In Advances in Neural Information Processing Systems (NIPS), 2009.
A General Projection Property for Distribution Families [pdf] paper   link   bibtex   1 download  
  2010 (1)
Relaxed Clipping: A Global Training Method for Robust Regression and Classification. Yu, Y.; Yang, M.; Xu, L.; White, M.; and Schuurmans, D. In Advances in Neural Information Processing Systems (NIPS), 2010.
Relaxed Clipping: A Global Training Method for Robust Regression and Classification [pdf] paper   link   bibtex  
  2011 (3)
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. Yu, Y.; and Schuurmans, D. In Conference on Uncertainty in Artificial Intelligence (UAI), 2011.
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering [pdf] paper   link   bibtex  
Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. Zhang, X.; Yu, Y.; White, M.; Huang, R.; and Schuurmans, D. In Association for the Advancement of Artificial Intelligence (AAAI), 2011.
Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions [pdf] paper   link   bibtex  
Distance Metric Learning by Minimal Distance Maximization. Yu, Y.; Jiang, J.; and Zhang, L. Pattern Recognition, 44: 639–649. 2011.
Distance Metric Learning by Minimal Distance Maximization [pdf] paper   link   bibtex  
  2012 (5)
Accelerated Training for Matrix-Norm Regularization: A Boosting Approach. Zhang, X.; Yu, Y.; and Schuurmans, D. In Advances in Neural Information Processing Systems (NIPS), 2012.
Accelerated Training for Matrix-Norm Regularization: A Boosting Approach [pdf] paper   link   bibtex  
Convex Multi-view Subspace Learning. White, M.; Yu, Y.; Zhang, X.; and Schuurmans, D. In Advances in Neural Information Processing Systems (NIPS), 2012.
Convex Multi-view Subspace Learning [pdf] paper   link   bibtex  
A Polynomial-time Form of Robust Regression. Yu, Y.; Aslan, Ö.; and Schuurmans, D. In Advances in Neural Information Processing Systems (NIPS), 2012.
A Polynomial-time Form of Robust Regression [pdf] paper   link   bibtex  
Regularizers versus Losses for Nonlinear Dimensionality Reduction. Yu, Y.; Neufeld, J.; Kiros, R.; Zhang, X.; and Schuurmans, D. In International Conference on Machine Learning (ICML), 2012.
Regularizers versus Losses for Nonlinear Dimensionality Reduction [pdf] paper   link   bibtex  
Analysis of Kernel Mean Matching under Covariate Shift. Yu, Y.; and Szepesvári, C. In International Conference on Machine Learning (ICML), 2012.
Analysis of Kernel Mean Matching under Covariate Shift [pdf] paper   link   bibtex   1 download  
  2013 (4)
Better Approximation and Faster Algorithm Using the Proximal Average. Yu, Y. In Advances in Neural Information Processing Systems (NIPS), 2013.
Better Approximation and Faster Algorithm Using the Proximal Average [pdf] paper   link   bibtex  
On Decomposing the Proximal Map. Yu, Y. In Advances in Neural Information Processing Systems (NIPS), 2013.
On Decomposing the Proximal Map [pdf] paper   link   bibtex  
Polar Operators for Structured Sparse Estimation. Zhang, X.; Yu, Y.; and Schuurmans, D. In Advances in Neural Information Processing Systems (NIPS), 2013.
Polar Operators for Structured Sparse Estimation [pdf] paper   link   bibtex  
Characterizing the Representer Theorem. Yu, Y.; Cheng, H.; Schuurmans, D.; and Szepesvári, C. In International Conference on Machine Learning (ICML), 2013.
Characterizing the Representer Theorem [pdf] paper   link   bibtex  
  2014 (1)
Efficient Structured Matrix Rank Minimization. Yu, A.; Ma, W.; Yu, Y.; Carbonell, J.; and Sra, S. In Advances in Neural Information Processing Systems (NIPS), 2014.
Efficient Structured Matrix Rank Minimization [pdf] paper   link   bibtex  
  2015 (6)
Petuum: A New Platform for Distributed Machine Learning on Big Data. Xing, E.; Ho, Q.; Dai, W.; Kim, J.; Wei, J.; Lee, S.; Zheng, X.; Xie, P.; Kumar, A.; and Yu, Y. IEEE Transactions on Big Data, 1(2): 49–67. 2015.
Petuum: A New Platform for Distributed Machine Learning on Big Data [pdf] paper   link   bibtex  
Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision. Chang, X.; Yu, Y.; Yang, Y.; and Hauptmann, A. In ACM Conference on Multimedia (MM), 2015.
Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision [pdf] paper   link   bibtex  
Linear Time Samplers for Supervised Topic Models using Compositional Proposals. Zheng, X.; Yu, Y.; and Xing, E. In ACM Conference on Knowledge Discovery and Data Mining (KDD), 2015.
Linear Time Samplers for Supervised Topic Models using Compositional Proposals [pdf] paper   link   bibtex  
Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. Chang, X.; Yang, Y.; Hauptmann, A.; Xing, E.; and Yu, Y. In International Joint Conference on Artificial Intelligence (IJCAI), 2015.
Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection [pdf] paper   link   bibtex  
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. Chang, X.; Yang, Y.; Xing, E.; and Yu, Y. In International Conference on Machine Learning (ICML), 2015.
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM [pdf] paper   link   bibtex  
Minimizing Nonconvex Non-Separable Functions. Yu, Y.; Zheng, X.; Marchetti-Bowick, M.; and Xing, E. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2015.
Minimizing Nonconvex Non-Separable Functions [pdf] paper   link   bibtex  
  2016 (4)
Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers. Chang, X.; Yu, Y.; Yang, Y.; and Xing, E. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
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Closed-Form Training of Mahalanobis Metric for Supervised Clustering. Law, M.; Yu, Y.; Cord, M.; and Xing, E. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
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Scalable and Sound Low-Rank Tensor Learning. Cheng, H.; Yu, Y.; Zhang, X.; Xing, E. P.; and Schuurmans, D. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
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On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. Zhou, Y.; Yu, Y.; Dai, W.; Liang, Y.; and Xing, E. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
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  preprint (3)
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. Kandasamy, K.; and Yu, Y. 2015. Submitted
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Semantic Pooling for Untrimmed Video Analysis. Chang, X.; Yu, Y.; Yang, Y.; and Xing, E. Nov 2015. Submitted
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Generalized Conditional Gradient for Sparse Estimation. Yu, Y.; Zhang, X.; and Schuurmans, D. 2014. Submitted
Generalized Conditional Gradient for Sparse Estimation [pdf] paper   link   bibtex